A machine learning approach to personalized predictors of dyslipidemia: a cohort study.

ABSTRACT:

Introduction

Mexico ranks second in the global prevalence of obesity in the adult population, which increases the probability of developing dyslipidemia. Dyslipidemia is closely related to cardiovascular diseases, which are the leading cause of death in the country. Therefore, developing tools that facilitate the prediction of dyslipidemias is essential for prevention and early treatment.

Methods

In this study, we utilized a dataset from a Mexico City cohort consisting of 2,621 participants, men and women aged between 20 and 50 years, with and without some type of dyslipidemia. Our primary objective was to identify potential factors associated with different types of dyslipidemia in both men and women. Machine learning algorithms were employed to achieve this goal. To facili

SUBMITTER: Gutierrez-Esparza G 

PROVIDER: S-EPMC10548235 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature